S3LI Interface

This package provides useful tools and scripts for playback and preparation of the S3LI dataset.

This repo relates to the S3LI Vulcano release, for the Etna dataset, go back to the main branch.

Links:

• https://rmc.dlr.de/s3li_dataset <-- Download dataset here!
• https://arxiv.org/abs/2601.19557
• https://ieeexplore.ieee.org/document/9813579

If you find this package useful for your work, consider to cite

@article{giubilato2026s3li,
  title={The S3LI Vulcano Dataset: A Dataset for Multi-Modal SLAM in Unstructured Planetary Environments},
  author={Giubilato, Riccardo and M{\"u}ller, Marcus Gerhard and Sewtz, Marco and Gonzalez, Laura Alejandra Encinar and Folkesson, John and Triebel, Rudolph},
  journal={2026 IEEE Aerospace Conference},
  year={2026}
}

@ARTICLE{9813579,
  author={Giubilato, Riccardo and Stürzl, Wolfgang and Wedler, Armin and Triebel, Rudolph},
  journal={IEEE Robotics and Automation Letters}, 
  title={Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset}, 
  year={2022},
  volume={7},
  number={4},
  pages={8721-8728},
  doi={10.1109/LRA.2022.3188118}
}

Generation of train and test datasets for place recognition

The package provides two scripts to scrim the bagfiles and generate datasets with synchronized tuples of: $$(I_{left}, L, p_{D-GNSS}, \phi_{north})$$ with $I_{left}$ the left camera image, $L$ a lidar scan, $p_{D-GNSS}$ the ground truth position in global coordinates, measured with a differential GNSS setup, and $\phi_{north}$ the orientation of the camera to the north.

1. Preparation

Download the dataset :) (https://datasets.arches-projekt.de/s3li_dataset/)

2. Set-Up

This code was tested using Python 3.12 on Ubuntu 24.04. To start, clone the repository and then use the provided requirements.txt to install the dependencies:

pip install -r requirements.txt

3. Pre-processing step: SLAM evaluation data to pickle

As the D-GNSS setup did not measure orientation of the camera, we approximate it using the results from the evaluated visual-inertial SLAM/Odometry algorithms. The idea is that, after aligning trajectories and camera poses to the D-GNSS ground truth, transformed into metric coordinates for an arbitrary ENU frame, the pose estimation should be accurate enough to provide a very good guess of the real orientatio of the cameras. Therefore, we can use this information to better discern between positive and negative samples for place recognition.

The script slam_poses_to_pandas.py reads the output .txt files from the Eval folder, and transforms them into a pandas dataframe. The dataframe will contain SLAM/VO results in the following tabular form:

Timestamp	Rotation	Position
posix_time [s]	Quaternion (w, x, y, z)	[x, y, z]

Usage:

1. From the root package folder: python3 scripts/slam_poses_to_pandas.py ${path_to_config} (e.g., python3 scripts/slam_poses_to_pandas.py cfg/config.yaml)
2. This will write pickle files in a folder names processed from the root dataset path

4. Sample Generation

This step foresees the generation of all data samples for each sequence. The script create_dataset.py reads the content of all bagfiles in the Bagfile folder, and associates data from the various topics, writing links and data into a pandas dataframe, then stored as a pickle to disk. In this step, the results of SLAM evaluations, pre-processed in Sec.2, are used to approximate an orientation (with respect to the North) for each image/scan pair, as the ground truth is only from D-GNSS without magnetometer or full RTK. To account for the yaw-drift, which is inevitable in the context of V-SLAM, camera trajectories are split in an user-defined manner (default is 3 splits) and independently aligned to the ground truth (x, y, z in ENU frame). The corresponding rotation is applied to the camera poses, and the northing is stored.

Usage:

1. From the package root: python3 scripts/create_dataset.py ${path_to_config} (e.g. python3 scripts/create_dataset.py cfg/config.yaml)

5. Lidar Histograms Generation

This script create_lidar_histograms.py processes LiDAR point cloud data stored in the pickle files and generates histograms representing the distribution of depth (Z-axis) values. The generated histograms will later be used in the visualization of the interactive plot.

Usage:

1. From the package root:python3 scripts/create_lidar_histograms.py ${path_to_config} `

6. Visualization

The results can be qualitatively inspected using the provided script make_maps_in_bokeh.py, which overlays subsampled images (with LiDAR point projections) on geo-references positions in an interactive map.

Usage:

1. From the package root: python3 scripts/make_maps_in_bokeh.py ${path_to_config}

This will look in the dataset folders for saved pickles, that contain a dataframe for each entire bagfile, and create an interactive Bokeh plot showing the global position and orientation of collected image/scan samples and pre-vieweing the image with lidar overlay when hovering with the mouse cursor.

Example Bokeh plot, full view:

Detail of the Bokeh plot, with data preview:

7. Interactive plot

Visual analysis interactive tool for camera overlap. The script interactive_overlap_maps.py calculates the overlap between different camera views using two possible methods:

• An angle-based approach (compute_overlap_v1)
• A polygon intersection approach (compute_overlap_v2)

In addition, it considers occlusion detection by analyzing LiDAR depth data to adjust camera field-of-view ranges.

Usage:

1. From the package root: python3 scripts/interactive_overlap_maps.py ${path_to_config} (e.g. python3 scripts/interactive_overlap_maps.py cfg/config.yaml)

Example Bokeh plot: